Semantic facial attribute editing using pre-trained Generative Adversarial Networks (GANs) has attracted a great deal of attention and effort from researchers in recent years. Due to the high quality of face images generated by StyleGANs, much work has focused on the StyleGANs' latent space and the proposed methods for facial image editing. Although these methods have achieved satisfying results for manipulating user-intended attributes, they have not fulfilled the goal of preserving the identity, which is an important challenge. We present ID-Style, a new architecture capable of addressing the problem of identity loss during attribute manipulation. The key components of ID-Style include Learnable Global Direction (LGD), which finds a shared and semi-sparse direction for each attribute, and an Instance-Aware Intensity Predictor (IAIP) network, which finetunes the global direction according to the input instance. Furthermore, we introduce two losses during training to enforce the LGD to find semi-sparse semantic directions, which along with the IAIP, preserve the identity of the input instance. Despite reducing the size of the network by roughly 95% as compared to similar state-of-the-art works, it outperforms baselines by 10% and 7% in Identity preserving metric (FRS) and average accuracy of manipulation (mACC), respectively.

翻译：利用预训练生成对抗网络（GANs）进行语义面部属性编辑近年来引起了研究者的广泛关注和大量投入。由于StyleGANs生成的人脸图像质量较高，大量工作聚焦于StyleGANs的潜在空间及所提出的面部图像编辑方法。尽管这些方法在操控用户预期属性方面取得了令人满意的结果，但未能实现身份保持的目标，这仍是一项重要的挑战。我们提出ID-Style，一种能够解决属性操控过程中身份丢失问题的新型架构。ID-Style的关键组件包括可学习全局方向（LGD），该模块为每个属性找到共享且半稀疏的编辑方向，以及实例感知强度预测器（IAIP）网络，该网络根据输入实例对全局方向进行微调。此外，我们在训练过程中引入两种损失函数，以强制LGD发现半稀疏语义方向，这些方向与IAIP共同保持输入实例的身份。与同类最先进工作相比，尽管网络规模缩小约95%，该方法在身份保持度量（FRS）和操控平均准确率（mACC）上分别超越基线10%和7%。